{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## AI电力能耗预测大赛实现案例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 小提示\n",
    "对于python的库和函数不熟悉的同学，可以百度搜索，也可以通过系统内置的help函数来查询哦。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on package pandas:\n",
      "\n",
      "NAME\n",
      "    pandas\n",
      "\n",
      "FILE\n",
      "    /usr/local/lib/python2.7/dist-packages/pandas/__init__.py\n",
      "\n",
      "DESCRIPTION\n",
      "    pandas - a powerful data analysis and manipulation library for Python\n",
      "    =====================================================================\n",
      "    \n",
      "    **pandas** is a Python package providing fast, flexible, and expressive data\n",
      "    structures designed to make working with \"relational\" or \"labeled\" data both\n",
      "    easy and intuitive. It aims to be the fundamental high-level building block for\n",
      "    doing practical, **real world** data analysis in Python. Additionally, it has\n",
      "    the broader goal of becoming **the most powerful and flexible open source data\n",
      "    analysis / manipulation tool available in any language**. It is already well on\n",
      "    its way toward this goal.\n",
      "    \n",
      "    Main Features\n",
      "    -------------\n",
      "    Here are just a few of the things that pandas does well:\n",
      "    \n",
      "      - Easy handling of missing data in floating point as well as non-floating\n",
      "        point data\n",
      "      - Size mutability: columns can be inserted and deleted from DataFrame and\n",
      "        higher dimensional objects\n",
      "      - Automatic and explicit data alignment: objects can  be explicitly aligned\n",
      "        to a set of labels, or the user can simply ignore the labels and let\n",
      "        `Series`, `DataFrame`, etc. automatically align the data for you in\n",
      "        computations\n",
      "      - Powerful, flexible group by functionality to perform split-apply-combine\n",
      "        operations on data sets, for both aggregating and transforming data\n",
      "      - Make it easy to convert ragged, differently-indexed data in other Python\n",
      "        and NumPy data structures into DataFrame objects\n",
      "      - Intelligent label-based slicing, fancy indexing, and subsetting of large\n",
      "        data sets\n",
      "      - Intuitive merging and joining data sets\n",
      "      - Flexible reshaping and pivoting of data sets\n",
      "      - Hierarchical labeling of axes (possible to have multiple labels per tick)\n",
      "      - Robust IO tools for loading data from flat files (CSV and delimited),\n",
      "        Excel files, databases, and saving/loading data from the ultrafast HDF5\n",
      "        format\n",
      "      - Time series-specific functionality: date range generation and frequency\n",
      "        conversion, moving window statistics, moving window linear regressions,\n",
      "        date shifting and lagging, etc.\n",
      "\n",
      "PACKAGE CONTENTS\n",
      "    _libs (package)\n",
      "    _version\n",
      "    api (package)\n",
      "    compat (package)\n",
      "    computation (package)\n",
      "    conftest\n",
      "    core (package)\n",
      "    errors (package)\n",
      "    formats (package)\n",
      "    io (package)\n",
      "    json\n",
      "    lib\n",
      "    parser\n",
      "    plotting (package)\n",
      "    stats (package)\n",
      "    testing\n",
      "    tests (package)\n",
      "    tools (package)\n",
      "    tseries (package)\n",
      "    tslib\n",
      "    types (package)\n",
      "    util (package)\n",
      "\n",
      "SUBMODULES\n",
      "    _hashtable\n",
      "    _lib\n",
      "    _tslib\n",
      "    offsets\n",
      "\n",
      "DATA\n",
      "    IndexSlice = <pandas.core.indexing._IndexSlice object>\n",
      "    NaT = NaT\n",
      "    __docformat__ = 'restructuredtext'\n",
      "    __version__ = u'0.20.1'\n",
      "    __warningregistry__ = {('numpy.dtype size changed, may indicate binary...\n",
      "    datetools = <module 'pandas.core.datetools' from '/usr/local...thon2.7...\n",
      "    describe_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    get_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    options = <pandas.core.config.DictWrapper object>\n",
      "    plot_params = {'xaxis.compat': False}\n",
      "    reset_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "    set_option = <pandas.core.config.CallableDynamicDoc object>\n",
      "\n",
      "VERSION\n",
      "    0.20.1\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "help(pd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 案例背景介绍"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](https://work.alibaba-inc.com/aliwork_tfs/g01_alibaba-inc_com/tfscom/TB1oAMfQFXXXXX1XVXXXXXXXXXX.tfsprivate.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "案例数据来源于江苏镇江扬中市的高新区企业历史近2年的用电量，希望能够根据历史数据去精准预测未来一个月每一天的用电量，这是一个很典型的回归类问题，和我们的流量预测非常相关，我们来看看如何用数据驱动的方式去完成这样一个预测。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 载入数据集合并"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "_df = pd.read_csv(\"./input/zhenjiang_power.csv\")\n",
    "df_201609 = pd.read_csv(\"./input/zhenjiang_power_9.csv\")\n",
    "train_df = pd.concat([_df, df_201609])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据一览\n",
    "数据会被读取成Excel一样行与列的形式。其中每一行是一个样本(一条历史记录)，每一列可以是一个指代特征(对最后结果有贡献的数据因素/维度)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>record_date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1135.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570.0</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3418.0</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3968.0</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3986.0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   power_consumption record_date  user_id\n",
       "0             1135.0  2015-01-01        1\n",
       "1              570.0  2015-01-02        1\n",
       "2             3418.0  2015-01-03        1\n",
       "3             3968.0  2015-01-04        1\n",
       "4             3986.0  2015-01-05        1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每一行是一条用电记录(一个企业一天一条记录)，每一列是不同的数据维度信息，第一列是用电量，第二列是日期，第三列是企业id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 929106 entries, 0 to 43619\n",
      "Data columns (total 3 columns):\n",
      "power_consumption    929106 non-null float64\n",
      "record_date          929106 non-null object\n",
      "user_id              929106 non-null int64\n",
      "dtypes: float64(1), int64(1), object(1)\n",
      "memory usage: 28.4+ MB\n"
     ]
    }
   ],
   "source": [
    "train_df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>9.291060e+05</td>\n",
       "      <td>929106.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.617557e+03</td>\n",
       "      <td>727.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.141672e+04</td>\n",
       "      <td>419.733772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.400000e+01</td>\n",
       "      <td>364.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2.630000e+02</td>\n",
       "      <td>727.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.280000e+02</td>\n",
       "      <td>1091.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.310016e+06</td>\n",
       "      <td>1454.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       power_consumption        user_id\n",
       "count       9.291060e+05  929106.000000\n",
       "mean        2.617557e+03     727.500000\n",
       "std         3.141672e+04     419.733772\n",
       "min         1.000000e+00       1.000000\n",
       "25%         4.400000e+01     364.000000\n",
       "50%         2.630000e+02     727.500000\n",
       "75%         8.280000e+02    1091.000000\n",
       "max         1.310016e+06    1454.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(929106, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 日期字段格式转化\n",
    "我们把字符串形式标示的日期列，转换成计算机能理解的日期格式，这样能方便取出对结果预测有帮助的相关信息(比如这一天是星期几，一个月的第几天，是否是工作日...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "train_df['record_date'] = pd.to_datetime(train_df['record_date'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>record_date</th>\n",
       "      <th>user_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1135.0</td>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>570.0</td>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3418.0</td>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3968.0</td>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3986.0</td>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   power_consumption record_date  user_id\n",
       "0             1135.0  2015-01-01        1\n",
       "1              570.0  2015-01-02        1\n",
       "2             3418.0  2015-01-03        1\n",
       "3             3968.0  2015-01-04        1\n",
       "4             3986.0  2015-01-05        1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据汇总聚合\n",
    "最终的目标是对每一天的用电量去进行预测，给定的数据是按小时维度拆分的，我们有不同的处理方式，简单的处理方式是对数据按天做一个汇总，用pandas做相关操作如下："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption\n",
       "0  2015-01-01          2900575.0\n",
       "1  2015-01-02          3158211.0\n",
       "2  2015-01-03          3596487.0\n",
       "3  2015-01-04          3939672.0\n",
       "4  2015-01-05          4101790.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = train_df[['record_date','power_consumption']].groupby(by='record_date').agg('sum')\n",
    "train_df = train_df.reset_index()\n",
    "train_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加测试数据\n",
    "我们要预测下一个月的每天用电量，我们从历史数据上用算法总结出来规律/模式以后，要把对应的规律/模式应用在未来的时间段进行预测，我们先生成未来的部分特征数据(这里主要是日期)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "test_df = pd.date_range('2016-10-1', periods=31, freq='D')\n",
    "test_df = pd.DataFrame(test_df)\n",
    "test_df.columns = ['record_date']\n",
    "test_df['power_consumption'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-10-01</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-10-02</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-10-03</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-10-04</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-10-05</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption\n",
       "0  2016-10-01                  0\n",
       "1  2016-10-02                  0\n",
       "2  2016-10-03                  0\n",
       "3  2016-10-04                  0\n",
       "4  2016-10-05                  0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 拼成总体数据\n",
    "我们把训练集和测试集拼接在一起，方便做一些统一的数据处理。（比如用电量一个很重要的因素是历史用电量，这个信息我们需要去做汇总计算和设置）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_df = pd.concat([train_df, test_df])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2016-10-27</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2016-10-28</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2016-10-29</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2016-10-30</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2016-10-31</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   record_date  power_consumption\n",
       "26  2016-10-27                0.0\n",
       "27  2016-10-28                0.0\n",
       "28  2016-10-29                0.0\n",
       "29  2016-10-30                0.0\n",
       "30  2016-10-31                0.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 构造和时间相关的强特征\n",
    "构造出一些强时间指代特征\n",
    "* 一周中的第几天(星期几)\n",
    "* 一个月中的第几天(月初还是月末的信息)\n",
    "* 一年中的第几天(年中年末等时间信息)\n",
    "* 一年中第几个月(季节相关的信息，季节会影响气温和用电)，哪一年。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_df['day_of_week'] = total_df['record_date'].apply(lambda x: x.dayofweek)\n",
    "total_df['day_of_month'] = total_df['record_date'].apply(lambda x: x.day)\n",
    "total_df['day_of_year'] = total_df['record_date'].apply(lambda x: x.dayofyear)\n",
    "total_df['month_of_year'] = total_df['record_date'].apply(lambda x: x.month)\n",
    "total_df['year'] = total_df['record_date'].apply(lambda x: x.year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  \n",
       "0              1  2015  \n",
       "1              1  2015  \n",
       "2              1  2015  \n",
       "3              1  2015  \n",
       "4              1  2015  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加周末特征信息\n",
    "添加工作日还是周末的信息，周六周日和工作日的用电量显然是不一样的(大家可以简单做一个数据统计就可以看出来)，这种时间维度的特征在我们流量的预测上同样有用哦！"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_df['holiday'] =0\n",
    "total_df['holiday_sat'] =0\n",
    "total_df['holiday_sun'] =0\n",
    "\n",
    "total_df.loc[total_df.day_of_week==5,'holiday'] = 1\n",
    "total_df.loc[total_df.day_of_week==5,'holiday_sat'] = 1\n",
    "\n",
    "total_df.loc[total_df.day_of_week==6,'holiday'] = 1\n",
    "total_df.loc[total_df.day_of_week==6,'holiday_sun'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  holiday  holiday_sat  holiday_sun  \n",
       "0              1  2015        0            0            0  \n",
       "1              1  2015        0            0            0  \n",
       "2              1  2015        1            1            0  \n",
       "3              1  2015        1            0            1  \n",
       "4              1  2015        0            0            0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 添加一个月4周的周信息\n",
    "一个月有4周，每一周的任务可能是不同的，对用电量也有影响，我们把一个月分成4周，来指代一个月的4个时间段。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def week_of_month(day):\n",
    "    if day in range(1,8): return 1\n",
    "    if day in range(8,15): return 2\n",
    "    if day in range(15,22): return 3\n",
    "    if day in range(22,32): return 4\n",
    "\n",
    "total_df['week_of_month'] = total_df['day_of_month'].apply(lambda x:week_of_month(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>week_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  holiday  holiday_sat  holiday_sun  week_of_month  \n",
       "0              1  2015        0            0            0              1  \n",
       "1              1  2015        0            0            0              1  \n",
       "2              1  2015        1            1            0              1  \n",
       "3              1  2015        1            0            1              1  \n",
       "4              1  2015        0            0            0              1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加上中下旬信息\n",
    "有些企业的任务是按照月份的上中下旬来安排的，同样可能对用电量会有影响，我们把一个月切成上中下旬3个部分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def period_of_month(day):\n",
    "    if day in range(1,11): return 1\n",
    "    if day in range(11,21): return 2\n",
    "    if day in range(21,32): return 3\n",
    "\n",
    "total_df['period_of_month'] = total_df['day_of_month'].apply(lambda x:period_of_month(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>period_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  holiday  holiday_sat  holiday_sun  period_of_month  \n",
       "0              1  2015        0            0            0                1  \n",
       "1              1  2015        0            0            0                1  \n",
       "2              1  2015        1            1            0                1  \n",
       "3              1  2015        1            0            1                1  \n",
       "4              1  2015        0            0            0                1  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加上半月下半月信息\n",
    "同上，添加上半月下半月的特征"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def period2_of_month(day):\n",
    "    if day in range(1,16): return 1\n",
    "    if day in range(16,32): return 2\n",
    "\n",
    "total_df['period2_of_month'] = total_df['day_of_month'].apply(lambda x:period2_of_month(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>period_of_month</th>\n",
       "      <th>period2_of_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  holiday  holiday_sat  holiday_sun  period_of_month  \\\n",
       "0              1  2015        0            0            0                1   \n",
       "1              1  2015        0            0            0                1   \n",
       "2              1  2015        1            1            0                1   \n",
       "3              1  2015        1            0            1                1   \n",
       "4              1  2015        0            0            0                1   \n",
       "\n",
       "   period2_of_month  \n",
       "0                 1  \n",
       "1                 1  \n",
       "2                 1  \n",
       "3                 1  \n",
       "4                 1  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 手动填充节日信息\n",
    "另外一个对用电量非常大的影响是节假日，法定节假日大部分企业会放假，电量会有大程度的下滑。我们通过查日历的方式去手动填充一个特征/字段，表明这一天是否是节日。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "total_df['festival_pc'] = 0\n",
    "total_df['festival'] = 0\n",
    "\n",
    "total_df.loc[total_df.record_date=='2016-10-01','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-01','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-01','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-01','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-02','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-02','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-02','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-02','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-03','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-03','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-03','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-03','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-04','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-04','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-04','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-04','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-05','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-05','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-05','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-05','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-06','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-06','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-06','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-06','holiday'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-07','festival_pc'] = total_df.loc[total_df.record_date=='2015-10-07','power_consumption'].values\n",
    "total_df.loc[total_df.record_date=='2016-10-07','festival'] = 1\n",
    "total_df.loc[total_df.record_date=='2016-10-07','holiday'] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>record_date</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>period_of_month</th>\n",
       "      <th>period2_of_month</th>\n",
       "      <th>festival_pc</th>\n",
       "      <th>festival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-01-01</td>\n",
       "      <td>2900575.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-01-02</td>\n",
       "      <td>3158211.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-01-03</td>\n",
       "      <td>3596487.0</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-01-04</td>\n",
       "      <td>3939672.0</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-01-05</td>\n",
       "      <td>4101790.0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2015</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  record_date  power_consumption  day_of_week  day_of_month  day_of_year  \\\n",
       "0  2015-01-01          2900575.0            3             1            1   \n",
       "1  2015-01-02          3158211.0            4             2            2   \n",
       "2  2015-01-03          3596487.0            5             3            3   \n",
       "3  2015-01-04          3939672.0            6             4            4   \n",
       "4  2015-01-05          4101790.0            0             5            5   \n",
       "\n",
       "   month_of_year  year  holiday  holiday_sat  holiday_sun  period_of_month  \\\n",
       "0              1  2015        0            0            0                1   \n",
       "1              1  2015        0            0            0                1   \n",
       "2              1  2015        1            1            0                1   \n",
       "3              1  2015        1            0            1                1   \n",
       "4              1  2015        0            0            0                1   \n",
       "\n",
       "   period2_of_month  festival_pc  festival  \n",
       "0                 1          0.0         0  \n",
       "1                 1          0.0         0  \n",
       "2                 1          0.0         0  \n",
       "3                 1          0.0         0  \n",
       "4                 1          0.0         0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 总体列名\n",
    "我们做了一些数据特征，来看一下现在的已经有的字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['record_date', 'power_consumption', 'day_of_week', 'day_of_month',\n",
       "       'day_of_year', 'month_of_year', 'year', 'holiday', 'holiday_sat',\n",
       "       'holiday_sun', 'period_of_month', 'period2_of_month', 'festival_pc',\n",
       "       'festival'], dtype=object)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col_names = total_df.columns.values\n",
    "col_names"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到有\n",
    "* 日期\n",
    "* 用电量\n",
    "* 星期几\n",
    "* 一个月第几天\n",
    "* 一年第几天\n",
    "* 一年第几个月\n",
    "* 年\n",
    "* 是否节假日\n",
    "* 月中第几周\n",
    "* 一个月上中下旬哪个旬 \n",
    "* 上半月还是下半月\n",
    "* 是否节日\n",
    "* ..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 确认一下咱们的训练数据没有缺省值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>festival</th>\n",
       "      <th>festival_pc</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>period2_of_month</th>\n",
       "      <th>period_of_month</th>\n",
       "      <th>power_consumption</th>\n",
       "      <th>record_date</th>\n",
       "      <th>week_of_month</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_of_month  day_of_week  day_of_year  festival  festival_pc  holiday  \\\n",
       "0             0            0            0         0            0        0   \n",
       "\n",
       "   holiday_sat  holiday_sun  month_of_year  period2_of_month  period_of_month  \\\n",
       "0            0            0              0                 0                0   \n",
       "\n",
       "   power_consumption  record_date  week_of_month  year  \n",
       "0                  0            0              0     0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts={}\n",
    "for name in col_names:\n",
    "    count = total_df[name].isnull().sum()\n",
    "    counts[name] = [count]\n",
    "pd.DataFrame(counts)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 添加独热向量编码/one-hot encoding\n",
    "对于类别型特征，我们经常在特征工程的时候会对他们做一些特殊的处理。\n",
    "一周总过有7天，有7个类别，最通用的处理方式叫做**独热向量编码**，我们会针对星期几这个特征，初始化一个长度为7的向量[0,0,0,0,0,0,0]\n",
    "\n",
    "* 星期一会被填充成[1,0,0,0,0,0,0]\n",
    "* 星期二会被填充成[0,1,0,0,0,0,0]\n",
    "* 星期三会被填充成[0,0,1,0,0,0,0]\n",
    "* 星期四会被填充成[0,0,0,1,0,0,0]\n",
    "* 以此类推...\n",
    "\n",
    "在树状模型中，一天七天的这种序列型数值数据，也可以由树状模型本身去探寻最好的且分点，因此也可以不做这个处理。（尤其是有一些机器学习库，比如lightGBM可以直接指定类别型变量，非常方便）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 树状模型建模"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "树状模型是工业界最常用的机器学习算法之一，我们在训练集上去学习出来一个最好的决策路径，而每条决策路径的根节点是我们预测的结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分离训练集和测试集\n",
    "我们根据日期分割训练集和测试集，用于后续的建模。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_X = total_df[~((total_df.year==2016)&(total_df.month_of_year==10))]\n",
    "test_X = total_df[((total_df.year==2016)&(total_df.month_of_year==10))]\n",
    "train_y = train_X.power_consumption\n",
    "train_X = train_X.drop(['power_consumption','record_date','year'],axis=1)\n",
    "test_X = test_X.drop(['power_consumption','record_date','year'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>day_of_month</th>\n",
       "      <th>day_of_year</th>\n",
       "      <th>month_of_year</th>\n",
       "      <th>holiday</th>\n",
       "      <th>holiday_sat</th>\n",
       "      <th>holiday_sun</th>\n",
       "      <th>week_of_month</th>\n",
       "      <th>period_of_month</th>\n",
       "      <th>period2_of_month</th>\n",
       "      <th>festival_pc</th>\n",
       "      <th>festival</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   day_of_week  day_of_month  day_of_year  month_of_year  holiday  \\\n",
       "0            3             1            1              1        0   \n",
       "1            4             2            2              1        0   \n",
       "2            5             3            3              1        1   \n",
       "3            6             4            4              1        1   \n",
       "4            0             5            5              1        0   \n",
       "\n",
       "   holiday_sat  holiday_sun  week_of_month  period_of_month  period2_of_month  \\\n",
       "0            0            0              1                1                 1   \n",
       "1            0            0              1                1                 1   \n",
       "2            1            0              1                1                 1   \n",
       "3            0            1              1                1                 1   \n",
       "4            0            0              1                1                 1   \n",
       "\n",
       "   festival_pc  festival  \n",
       "0          0.0         0  \n",
       "1          0.0         0  \n",
       "2          0.0         0  \n",
       "3          0.0         0  \n",
       "4          0.0         0  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(639, 12)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 建模与调参\n",
    "#### 我们利用网格搜索交叉验证去查找最好的参数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTreeRegressor(criterion='mse', max_depth=3, max_features=1,\n",
      "           max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
      "           splitter='best')\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "param_grid = {'max_features': [0.7, 0.8, 0.9, 1],\n",
    "              'max_depth':  [3, 5, 7, 9, 12]\n",
    "             }\n",
    "\n",
    "dt = DecisionTreeRegressor()\n",
    "\n",
    "grid = GridSearchCV(dt, param_grid=param_grid, cv=5, n_jobs=8, refit=True)\n",
    "grid.fit(train_X, train_y)\n",
    "best_dt_reg = grid.best_estimator_\n",
    "print best_dt_reg"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 考察一下训练集上的拟合程度"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14007941111856592"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_dt_reg.score(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行结果预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predict_date</th>\n",
       "      <th>predict_power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20161001</td>\n",
       "      <td>3820887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20161002</td>\n",
       "      <td>3845830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20161003</td>\n",
       "      <td>3845830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20161004</td>\n",
       "      <td>3845830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20161005</td>\n",
       "      <td>3845830</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  predict_date  predict_power_consumption\n",
       "0     20161001                    3820887\n",
       "1     20161002                    3845830\n",
       "2     20161003                    3845830\n",
       "3     20161004                    3845830\n",
       "4     20161005                    3845830"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime \n",
    "\n",
    "#完成提交日期格式的转换\n",
    "def dataprocess(t):\n",
    "    t = str(t)[0:10]\n",
    "    time = datetime.strptime(t, '%Y-%m-%d')\n",
    "    res = time.strftime('%Y%m%d')\n",
    "    return res\n",
    "\n",
    "#生成10月份31天的时间段\n",
    "commit_df = pd.date_range('2016/10/1', periods=31, freq='D')\n",
    "commit_df = pd.DataFrame(commit_df)\n",
    "commit_df.columns = ['predict_date']\n",
    "\n",
    "#用模型进行预测\n",
    "prediction = best_dt_reg.predict(test_X.values)\n",
    "commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')\n",
    "commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)\n",
    "commit_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 高级版本尝试：模型融合 \n",
    "### RandomForest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=3,\n",
      "           max_features=0.7, max_leaf_nodes=None, min_impurity_split=1e-07,\n",
      "           min_samples_leaf=1, min_samples_split=2,\n",
      "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,\n",
      "           oob_score=False, random_state=None, verbose=0, warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# RandomForestRegressor\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "param_grid = {\n",
    "              'n_estimators': [5, 8, 10, 15, 20, 50, 100, 200],\n",
    "              'max_depth': [3, 5, 7, 9],\n",
    "              'max_features': [0.6, 0.7, 0.8, 0.9],\n",
    "             }\n",
    "\n",
    "rf = RandomForestRegressor()\n",
    "\n",
    "grid = GridSearchCV(rf, param_grid=param_grid, cv=3, n_jobs=8, refit=True)\n",
    "grid.fit(train_X, train_y)\n",
    "\n",
    "breg = grid.best_estimator_\n",
    "print breg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.46477016113397185"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breg.score(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征重要度(归因)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.09260786,  0.08608125,  0.55249269,  0.21200127,  0.01640171,\n",
       "        0.01483865,  0.02133366,  0.00261841,  0.0016245 ,  0.        ,\n",
       "        0.        ,  0.        ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "breg.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "feature day_of_year (0.552493)\n",
      "feature month_of_year (0.212001)\n",
      "feature day_of_week (0.092608)\n",
      "feature day_of_month (0.086081)\n",
      "feature holiday_sun (0.021334)\n",
      "feature holiday (0.016402)\n",
      "feature holiday_sat (0.014839)\n",
      "feature week_of_month (0.002618)\n",
      "feature period_of_month (0.001625)\n",
      "feature festival (0.000000)\n",
      "feature festival_pc (0.000000)\n",
      "feature period2_of_month (0.000000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AwGAEQgAAAACDEQgBAAAADEYgBAAAADAYgRAAAADAYARCAAAAAIMRCAEAAAAMRiAEAAAA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\nyO7+0+5+1kFY11c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f302c707950>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Feature ranking:\")\n",
    "feature_names = [u'day_of_week', u'day_of_month', u'day_of_year', u'month_of_year',\n",
    "       u'holiday', u'holiday_sat', u'holiday_sun', u'week_of_month',\n",
    "       u'period_of_month', u'period2_of_month', u'festival_pc', u'festival']\n",
    "feature_importances = breg.feature_importances_\n",
    "indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "for f in indices:\n",
    "    print(\"feature %s (%f)\" % (feature_names[f], feature_importances[f]))\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(len(feature_importances)), feature_importances[indices],\n",
    "       color=\"b\",align=\"center\")\n",
    "plt.xticks(range(len(feature_importances)), np.array(feature_names)[indices])\n",
    "plt.xlim([-1, train_X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对10月份进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predict_date</th>\n",
       "      <th>predict_power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20161001</td>\n",
       "      <td>3748691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20161002</td>\n",
       "      <td>3557111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20161003</td>\n",
       "      <td>3819058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20161004</td>\n",
       "      <td>3819058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20161005</td>\n",
       "      <td>3819058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  predict_date  predict_power_consumption\n",
       "0     20161001                    3748691\n",
       "1     20161002                    3557111\n",
       "2     20161003                    3819058\n",
       "3     20161004                    3819058\n",
       "4     20161005                    3819058"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime \n",
    "\n",
    "def dataprocess(t):\n",
    "    t = str(t)[0:10]\n",
    "    time = datetime.strptime(t, '%Y-%m-%d')\n",
    "    res = time.strftime('%Y%m%d')\n",
    "    return res\n",
    "\n",
    "commit_df = pd.date_range('2016/10/1', periods=31, freq='D')\n",
    "commit_df = pd.DataFrame(commit_df)\n",
    "commit_df.columns = ['predict_date']\n",
    "prediction = breg.predict(test_X)\n",
    "commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')\n",
    "commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)\n",
    "\n",
    "commit_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GradientBoostingDecisionTree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们用xgboost这个工具库来完成建模，这是一个非常强大的GBM(Gradient Boosting Machine)集成模型库，速度也比较快。\n",
    "下面是使用这个库建模和调参的一个模板，大家可以参考一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.249983577326\n",
      "{'n_estimators': 20, 'subsample': 1, 'learning_rate': 0.3, 'max_depth': 3, 'colsample_bylevel': 0.8}\n"
     ]
    }
   ],
   "source": [
    "# 导入所需的库\n",
    "import os\n",
    "import numpy as np\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import xgboost as xgb\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "# 取出建模所用的训练数据矩阵和目标y\n",
    "X = train_X.values\n",
    "y = train_y.values\n",
    "\n",
    "# 候选参数，用于参数调优\n",
    "param_grid = {\n",
    "              'max_depth': [3, 4, 5, 7, 9],\n",
    "              'n_estimators': [20, 40, 50, 80, 100, 200, 400, 800, 1000, 1200],\n",
    "              'learning_rate': [0.05, 0.1, 0.2, 0.3],\n",
    "              'subsample': [0.8, 1],\n",
    "              'colsample_bylevel':[0.8, 1]\n",
    "             }\n",
    "\n",
    "# 使用xgboost的regressor完成回归\n",
    "xgb_model = xgb.XGBRegressor()\n",
    "# 数据拟合\n",
    "rgs = GridSearchCV(xgb_model, param_grid, n_jobs=8)\n",
    "rgs.fit(X, y)\n",
    "\n",
    "print(rgs.best_score_)\n",
    "print(rgs.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 进行预测"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>predict_date</th>\n",
       "      <th>predict_power_consumption</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20161001</td>\n",
       "      <td>3085395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20161002</td>\n",
       "      <td>3010870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20161003</td>\n",
       "      <td>3390123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20161004</td>\n",
       "      <td>3390123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20161005</td>\n",
       "      <td>3696733</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  predict_date  predict_power_consumption\n",
       "0     20161001                    3085395\n",
       "1     20161002                    3010870\n",
       "2     20161003                    3390123\n",
       "3     20161004                    3390123\n",
       "4     20161005                    3696733"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime \n",
    "\n",
    "def dataprocess(t):\n",
    "    t = str(t)[0:10]\n",
    "    time = datetime.strptime(t, '%Y-%m-%d')\n",
    "    res = time.strftime('%Y%m%d')\n",
    "    return res\n",
    "\n",
    "commit_df = pd.date_range('2016/10/1', periods=31, freq='D')\n",
    "commit_df = pd.DataFrame(commit_df)\n",
    "commit_df.columns = ['predict_date']\n",
    "prediction = rgs.predict(test_X.values)\n",
    "commit_df['predict_power_consumption'] = pd.DataFrame(prediction).astype('int')\n",
    "commit_df['predict_date'] = commit_df['predict_date'].apply(dataprocess)\n",
    "\n",
    "commit_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=0.8,\n",
       "       colsample_bytree=1, gamma=0, learning_rate=0.3, max_delta_step=0,\n",
       "       max_depth=3, min_child_weight=1, missing=None, n_estimators=20,\n",
       "       n_jobs=1, nthread=1, objective='reg:linear', random_state=0,\n",
       "       reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=0, silent=True,\n",
       "       subsample=1)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb_model = xgb.XGBRegressor(n_estimators=20, subsample=1, learning_rate=0.3, max_depth=3, colsample_bylevel=0.8)\n",
    "xgb_model.fit(train_X, train_y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征重要度(归因)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Feature ranking:\n",
      "feature day_of_year (0.508333)\n",
      "feature day_of_month (0.233333)\n",
      "feature day_of_week (0.125000)\n",
      "feature month_of_year (0.108333)\n",
      "feature holiday (0.016667)\n",
      "feature week_of_month (0.008333)\n",
      "feature festival (0.000000)\n",
      "feature festival_pc (0.000000)\n",
      "feature period2_of_month (0.000000)\n",
      "feature period_of_month (0.000000)\n",
      "feature holiday_sun (0.000000)\n",
      "feature holiday_sat (0.000000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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AwGAEQgAAAACDEQgBAAAADEYgBAAAADAYgRAAAADAYARCAAAAAIMRCAEAAAAMRiAEAAAA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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f301a9762d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Feature ranking:\")\n",
    "feature_names = [u'day_of_week', u'day_of_month', u'day_of_year', u'month_of_year',\n",
    "       u'holiday', u'holiday_sat', u'holiday_sun', u'week_of_month',\n",
    "       u'period_of_month', u'period2_of_month', u'festival_pc', u'festival']\n",
    "feature_importances = xgb_model.feature_importances_\n",
    "indices = np.argsort(feature_importances)[::-1]\n",
    "\n",
    "for f in indices:\n",
    "    print(\"feature %s (%f)\" % (feature_names[f], feature_importances[f]))\n",
    "\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.title(\"Feature importances\")\n",
    "plt.bar(range(len(feature_importances)), feature_importances[indices],\n",
    "       color=\"b\",align=\"center\")\n",
    "plt.xticks(range(len(feature_importances)), np.array(feature_names)[indices])\n",
    "plt.xlim([-1, train_X.shape[1]])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制出前几棵树决策过程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f301ca617d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "xgb.plot_tree(xgb_model, num_trees=10)\n",
    "fig = plt.gcf()\n",
    "fig.set_size_inches(150, 100)\n",
    "fig.savefig('tree.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
